The role of green bonds on industrial sustainability for achieving carbon neutrality: Evidence from the artificial neural network method

IF 6.3 2区 经济学 Q1 BUSINESS, FINANCE Research in International Business and Finance Pub Date : 2024-11-23 DOI:10.1016/j.ribaf.2024.102659
Chi Keung Lau , Hemachandra Padhan , Amit Kumar Das , Aviral Kumar Tiwari , Giray Gozgor , Preksha Jain
{"title":"The role of green bonds on industrial sustainability for achieving carbon neutrality: Evidence from the artificial neural network method","authors":"Chi Keung Lau ,&nbsp;Hemachandra Padhan ,&nbsp;Amit Kumar Das ,&nbsp;Aviral Kumar Tiwari ,&nbsp;Giray Gozgor ,&nbsp;Preksha Jain","doi":"10.1016/j.ribaf.2024.102659","DOIUrl":null,"url":null,"abstract":"<div><div>This paper examines the role of green bonds on industrial sustainability in 15 Organisation for Economic Co-operation and Development (OECD) economies from 2010 to 2020. In this context, we utilise the Augmented Mean Group (AMG), the Artificial Neural Network (ANN), and the Kernel-based Regularised Least Squares (KRLS) methods. It is found that the ANN predicts the influence of green bonds on industrial sustainability more accurately than other methods. It is also observed that green bonds accelerate industrial sustainability in the OECD economies. The upper percentile group is primarily concerned with industrial sustainability rather than the lower- and middle percentile groups. Therefore, the OECD economies should emphasise the green bonds component in the green finance baskets to achieve carbon neutrality.</div></div>","PeriodicalId":51430,"journal":{"name":"Research in International Business and Finance","volume":"73 ","pages":"Article 102659"},"PeriodicalIF":6.3000,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research in International Business and Finance","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0275531924004525","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
引用次数: 0

Abstract

This paper examines the role of green bonds on industrial sustainability in 15 Organisation for Economic Co-operation and Development (OECD) economies from 2010 to 2020. In this context, we utilise the Augmented Mean Group (AMG), the Artificial Neural Network (ANN), and the Kernel-based Regularised Least Squares (KRLS) methods. It is found that the ANN predicts the influence of green bonds on industrial sustainability more accurately than other methods. It is also observed that green bonds accelerate industrial sustainability in the OECD economies. The upper percentile group is primarily concerned with industrial sustainability rather than the lower- and middle percentile groups. Therefore, the OECD economies should emphasise the green bonds component in the green finance baskets to achieve carbon neutrality.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
绿色债券对实现碳中和的工业可持续性的作用:来自人工神经网络方法的证据
本文研究了 2010 年至 2020 年期间绿色债券对经济合作与发展组织(OECD)15 个经济体的工业可持续发展所起的作用。在此背景下,我们采用了增强均值组 (AMG)、人工神经网络 (ANN) 和基于核的正则最小二乘法 (KRLS) 方法。研究发现,与其他方法相比,人工神经网络能更准确地预测绿色债券对工业可持续性的影响。研究还发现,绿色债券加速了经合组织经济体的工业可持续发展。高百分位组主要关注工业可持续性,而不是低百分位组和中百分位组。因此,经合组织经济体应重视绿色金融篮子中的绿色债券部分,以实现碳中和。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
CiteScore
11.20
自引率
9.20%
发文量
240
期刊介绍: Research in International Business and Finance (RIBAF) seeks to consolidate its position as a premier scholarly vehicle of academic finance. The Journal publishes high quality, insightful, well-written papers that explore current and new issues in international finance. Papers that foster dialogue, innovation, and intellectual risk-taking in financial studies; as well as shed light on the interaction between finance and broader societal concerns are particularly appreciated. The Journal welcomes submissions that seek to expand the boundaries of academic finance and otherwise challenge the discipline. Papers studying finance using a variety of methodologies; as well as interdisciplinary studies will be considered for publication. Papers that examine topical issues using extensive international data sets are welcome. Single-country studies can also be considered for publication provided that they develop novel methodological and theoretical approaches or fall within the Journal''s priority themes. It is especially important that single-country studies communicate to the reader why the particular chosen country is especially relevant to the issue being investigated. [...] The scope of topics that are most interesting to RIBAF readers include the following: -Financial markets and institutions -Financial practices and sustainability -The impact of national culture on finance -The impact of formal and informal institutions on finance -Privatizations, public financing, and nonprofit issues in finance -Interdisciplinary financial studies -Finance and international development -International financial crises and regulation -Financialization studies -International financial integration and architecture -Behavioral aspects in finance -Consumer finance -Methodologies and conceptualization issues related to finance
期刊最新文献
Registration reform and stock mispricing: Causal inference based on double machine learning The role of green bonds on industrial sustainability for achieving carbon neutrality: Evidence from the artificial neural network method Zero-leverage and firm performance – Evidence from Taiwan The green premium of unconventional monetary policy: Evidence from the enlarged collateral framework by the People's Bank of China Booster or trapper? Corporate digital transformation and capital allocation efficiency
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1